Removing multiplicative noise using l1 data-fidelity term and nonlocal total variation
Xiao Bing Shang, Zhi long Zhao, Lin Yang · 2012
In this paper, we consider a hybrid method for removing multiplicative noise e.g. speckle noise. Our model consists of l1 data-fidelity term and the nonlocal total variation as regularizer. The l1 data-fidelity term can preserve edges during despecking framework in the curvelet domain. We import the nonlocal total variation as regularizer which can recover the textures and local geometry structures. Moreover, the efficiency of the algorithm adopted here is based on operator Augmented Lagrangian for the hybrid method. Experiments show that the proposed scheme outperforms the most recent methods in this field.